Best Data Classification Software for Google Cloud BigQuery

Find and compare the best Data Classification software for Google Cloud BigQuery in 2024

Use the comparison tool below to compare the top Data Classification software for Google Cloud BigQuery on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    MineOS Reviews
    MineOS is a UX-focused data privacy platform that knows compliance work has been rough in the past. Our automated no-code platform can be up & running in a day to bring companies comprehensive data mapping & classification that integrates with hundreds of popular data sources and discovers nearly 100% of a company’s data. By mapping and classifying data better, DSR management, risk assessments, and data policy enforcement all become easier and faster. Simplify compliance. Gain consumer trust. MineOS.ai
  • 2
    Protegrity Reviews
    Our platform allows businesses to use data, including its application in advanced analysis, machine learning and AI, to do great things without worrying that customers, employees or intellectual property are at risk. The Protegrity Data Protection Platform does more than just protect data. It also classifies and discovers data, while protecting it. It is impossible to protect data you don't already know about. Our platform first categorizes data, allowing users the ability to classify the type of data that is most commonly in the public domain. Once those classifications are established, the platform uses machine learning algorithms to find that type of data. The platform uses classification and discovery to find the data that must be protected. The platform protects data behind many operational systems that are essential to business operations. It also provides privacy options such as tokenizing, encryption, and privacy methods.
  • 3
    iDiscover Reviews
    Mage Sensitive Data Discovery module can help you uncover hidden data locations in your company. You can find data hidden in any type of data store, whether it is structured, unstructured or Big Data. Natural Language Processing and Artificial Intelligence can be used to find data in the most difficult of places. A patented approach to data discovery ensures efficient identification of sensitive data and minimal false positives. You can add data classifications to your existing 70+ data classifications that cover all popular PII/PHI data. A simplified discovery process allows you to schedule sample, full, and even incremental scans.
  • 4
    Nightfall Reviews
    Protect your sensitive data by identifying, classifying, and classifying it. Nightfall™, which uses machine learning to identify sensitive data such as customer PII across your SaaS, APIs and data infrastructure, allows you to manage and protect it. To monitor your data, integrate with cloud services via APIs in minutes. Machine learning accurately classifies sensitive data and PII, so nothing is missed. Automated workflows can be set up to save time and keep your business safe. Nightfall integrates directly to all your SaaS, APIs and data infrastructure. Nightfall APIs are available for sensitive data protection and classification. You can programmatically access structured results from Nightfall’s deep learning-based detectors, such as API keys and credit card numbers. Just a few lines of code are required to integrate. Nightfall's REST API allows you to easily add data classifications to your applications and workflows.
  • 5
    NVISIONx Reviews
    The NVISIONx data intelligence platform allows companies to take control of their enterprise data to reduce risks, compliance scopes and storage costs. Data is growing rapidly and getting worse every single day. Security and business leaders are overwhelmed and cannot protect what they don’t know. The problem can't be solved by adding more controls. Rich and unlimited analysis to support more than 150 business use cases to empower cyber professionals and data owners to manage their data proactive from cradle through grave. To reduce storage costs and the scope of data classification, first categorize or group redundant, obsolete, or trivial data (ROT). Next, use a variety of data analytics techniques to contextually classify any remaining data. This will allow the data owner to become their own data analyst. The legal and records retention reviews can be used to identify data that is not needed or desired.
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